A kit for single cell gene expression analysis

单细胞基因表达分析试剂盒

• 批准号: 8446257
• 负责人: Glenn Fu
• 金额: $ 15万
• 依托单位: CELLULAR RESEARCH, INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8446257
• 关键词: Achievement; Area; Biological Assay; Cell physiology; Cells; Collaborations; Complementary DNA; DNA; DNA Sequence; DNA amplification; Disease; Engineering; Gene Expression; Gene Expression Profiling; Genes; Genome; Genomics; Goals; Health; Human Genome; Individual; Label; Measurement; Measures; Messenger RNA; Methodology; Methods; Molecular; Nucleic Acids; Phase; Process; Protocols documentation; Publications; Publishing; Reading; Reagent; Relative (related person); Research; Research Personnel; Reverse Transcription; Sampling; Scientist; Sequence Analysis; Techniques; Technology; Testing; Transcript; Universities; base; commercialization; design; instrument; member; novel; novel strategies; public health relevance; research facility; single cell analysis; single molecule

DESCRIPTION (provided by applicant): The study of gene expression is fundamental to our understanding of how cells function. We propose to develop a highly sensitive and precise method to measure global gene expression in single cells. Our novel method is simple, yet is capable of accurately counting individual transcripts across all genes expressed within a single cell. The measurements obtained are absolute numbers of gene transcripts, and represents a significant improvement over modern gene expression techniques which typically only provide relative measurements. Our method is based on the concept of "stochastic labeling" that we recently published where we validated the novel approach by randomly labeling every single copy of fragments of genomic DNA in a sample with a set of molecular barcodes. Once labeled, the original DNA fragments were amplified with PCR and detected by massively parallel sequencing. Counting the number of different barcodes reveals the number of original copies of that DNA fragment, easily distinguishing the plurality of copies of a fragment of identical DNA sequence from additional clones of itself created by PCR replication. We successfully transform the challenging task of counting identical copies of single DNA molecules into the simple process of identifying the number of different barcodes present on identical sequences. Our current objective for phase I is to develop the technique into an application for single cell gene expression analysis where quantitative measurements are especially difficult due to the small amount of starting material present. Another significant challenge in single cell analysis is that the high degree of DNA amplification required creates biases in the gene abundance representation. We circumvent the effects of amplitude distortions by barcoding molecules before any amplification steps, and counting barcodes instead of sequence reads to determine the number of original molecules present. For phase II, we will develop a commercial assay kit containing reagents and protocols to carry out the technique. Our company is comprised of an exceptionally strong team of successful innovators and scientists/engineers with significant achievements in both research and product commercialization settings. Members include an established investigator in the area of single cell/single molecule research, and a key inventor and developer of the most widely used gene expression platform over the past decade. Additionally, we have established collaborations with scientists at the Stanford University Genome Technology Center, giving us access to instruments and expertise available at this world class research facility.

描述(由申请人提供)：对基因表达的研究是我们理解细胞功能的基础。我们建议开发一种高灵敏度和高精度的方法来测量单个细胞中的全球基因表达。我们的新方法很简单，但能够准确地计算单个细胞内表达的所有基因的单个转录本。所获得的测量是基因转录本的绝对数量，与通常只提供相对测量的现代基因表达技术相比，这是一个重大改进。我们的方法是基于我们最近发表的“随机标记”的概念，我们通过用一组分子条形码随机标记样本中基因组DNA片段的每一个拷贝来验证新方法。标记后，用聚合酶链式反应扩增原始DNA片段，并进行大规模平行测序。计算不同条形码的数量可以揭示该DNA片段的原始副本的数量，从而很容易将相同DNA序列片段的多个副本与通过PCR复制创建的自身的其他克隆区分开来。我们成功地将计算单个DNA分子相同拷贝的挑战性任务转化为识别相同序列上存在的不同条形码数量的简单过程。我们目前第一阶段的目标是将这项技术发展成单细胞基因表达分析的应用程序，在这种情况下，由于存在少量的起始材料，定量测量特别困难。单细胞分析中的另一个重大挑战是所需的高度DNA扩增在基因丰度表示中产生了偏差。我们在任何扩增步骤之前对分子进行条形码编码，并对条形码而不是序列读数进行计数，以确定存在的原始分子的数量，从而绕过幅度失真的影响。对于第二阶段，我们将开发一种商业分析试剂盒，其中包含实施该技术的试剂和方案。我们公司由一支由成功的创新者和科学家/工程师组成的特别强大的团队组成，他们在研究和产品商业化方面都取得了重大成就。成员包括单细胞/单分子研究领域的一名知名研究员，以及过去十年来最广泛使用的基因表达平台的主要发明者和开发商。此外，我们还与斯坦福大学基因组技术中心的科学家建立了合作关系，使我们能够使用这一世界级研究设施提供的仪器和专业知识。